Using a nanoscale silica fiber taper,light can be efficiently coupled into a single ZnO nanowire by means of evanescent coupling. The method is valid for launching light into a single nanowire in the ultraviolet to in...Using a nanoscale silica fiber taper,light can be efficiently coupled into a single ZnO nanowire by means of evanescent coupling. The method is valid for launching light into a single nanowire in the ultraviolet to infrared range with a coupling efficiency of 25%, Low-loss optical guiding of ZnO nanowires is demonstrated, and the photoluminescence of a single ZnO nanowire is also observed. Compared to conventional approaches in which a lensfocused laser beam is used to excite nanowires at specific wavelengths,this evanescent coupling approach has advantages such as high coupling efficiency and broad-band validity, and it is promising for the optical characterization of semiconductor nanowires or nanoribbons.展开更多
TiO2 thin film has attracted considerable attention in recent years, due to its different refractive index and transparency with amorphous and different crysta ls in the visible and near-infrared wavelength region, hi...TiO2 thin film has attracted considerable attention in recent years, due to its different refractive index and transparency with amorphous and different crysta ls in the visible and near-infrared wavelength region, high dielectric constant, wide band gap, high wear resistance and stability, etc, for which make it being used in many fields. This paper aims to investigate the optical characterizatio n of thin film TiO2 on silicon wafer. The TiO2 thin films were prepared by DC re active magnetron sputtering process from Ti target. The reflectivity of the film s was measured by UV-3101PC, and the index of refraction (n) and extinction coef ficient (k) were measured by n & k Analyzer 1200.展开更多
Two-photon polymerization lithography is a technique that provides hundreds of nanometer resolution and full geometric freedom.Several X-ray polymer refractive lenses created by this technique were characterized using...Two-photon polymerization lithography is a technique that provides hundreds of nanometer resolution and full geometric freedom.Several X-ray polymer refractive lenses created by this technique were characterized using differential phase contrast imaging(DPCI)with a microfocus X-ray grating interferometer.The beam deflection angle and wavefront phase shift of the X-ray beam through the lens were obtained.Comparative tests using synchrotron radiation sources showed that the system could measure the surface shape of X-ray refractive lenses with an accuracy of 0.4μm.This study is important for improving the fabrication process and focusing performance of X-ray refractive lenses.展开更多
Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have bee...Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have been proposed,most of them focus on recognizing printed Urdu characters and digits.To the best of our knowledge,very little research has focused solely on Urdu pure handwriting recognition,and the results of such proposed methods are often inadequate.In this study,we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks(CNN).Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers,enabling efficient and accurate detection of Urdu handwritten digits and characters.We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters.The findings demonstrate that the CNN model achieves an accuracy of 98.30%and an F1 score of 88.6%,indicating its effectiveness in detecting and classifyingUrdu handwritten digits and characters.These results have far-reaching implications for various applications,including document analysis,text recognition,and language understanding,which have previously been unexplored in the context of Urdu handwriting data.This work lays a solid foundation for future research and development in Urdu language detection and processing,opening up new opportunities for advancement in this field.展开更多
The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children a...The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.展开更多
The confocal microscopy technique was applied for nonlinear optical characterization of single β-barium-borate（β-BBO） nanocrystals. The experimental setup allows measurements of the laser polarization-selective se...The confocal microscopy technique was applied for nonlinear optical characterization of single β-barium-borate（β-BBO） nanocrystals. The experimental setup allows measurements of the laser polarization-selective secondharmonic（SH） generation, and the results can be used to determine the nanocrystals＇ c-axis orientation, as well as to obtain information about their second-order susceptibility χ^（2）. The dependence of the SH signal on the laser polarization allowed the discrimination of individual particles from aggregates. The data were fitted using a model that takes into account the BBO properties and the experimental setup characteristics considering（i） the electrostatic approximation,（ii） the effects of the microscope objective used to focus the light on the sample in an epi-geometry configuration, and（iii） the symmetry of χ^（2） for the β-BBO nanocrystals. A signal at the third-harmonic frequency was also detected, but it was too weak to be studied in detail.展开更多
ZnO nanoparticles(ZnO-NP)present innovative optical,electrical,and magnetic properties that depend on spe-cific characteristics,e.g.,size,distribution,and morphology.Thus,these properties are essential to address vari...ZnO nanoparticles(ZnO-NP)present innovative optical,electrical,and magnetic properties that depend on spe-cific characteristics,e.g.,size,distribution,and morphology.Thus,these properties are essential to address various applications in areas such as electronics,medicine,energy,and others.In addition,the performance of this ZnO-NP depends of their preparation which can be done by chemical,physical,and biological methods.Meanwhile,nowadays,the main interest in developing ZnO-NP synthesis through biological methods bases on the decrease of use of toxic chemicals or energy applied to the procedures,making the process more cost-effective and environ-mentally friendly.However,the large-scale production of nanoparticles by green synthesis remains a big challenge due to the complexity of the biological extracts used in chemical reactions.That being the case,the preparation of ZnO-NP using Moringa oleifera extract as an alternative biological agent for capping and reduction in synthesis was evaluated in this work.Then,the results based on the analysis of the optical and structural characterization of the ZnO-NP obtained by employing UV-Vis,DLS,zeta potential,XRD,ATR-FTIR,and FE-SEM indicate mostly the presence of spherical nanosized material with a mean hydrodynamic diameter of 47.2 nm measured by DLS and a mean size diameter of 25 nm observed with FE-SEM technique.Furthermore,in FE-SEM images a homo-geneous dispersion and distribution is observed in the absence of agglutination,agglomeration,or generation of significant lumps of the ZnO-NP.The XRD analysis showed that heat annealing induced the crystallite size favor-ing their monocrystallinity.Those obtained data confirm the synthesis of ZnO-NP and the absence of impurities associated with organic compounds in the annealed samples.Finally,those results and low-cost production pre-sent to the synthesized ZnO-NP by this biological method as a useful material in several applications.展开更多
This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingl...This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.展开更多
Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effe...Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.展开更多
Using transmission electron microscopy （TEM） and x-ray diffraction analysis, we have studied the structural and morphological evolution of highly Er/Yb co-doped A1203 films in the temperature range from 600℃-900℃....Using transmission electron microscopy （TEM） and x-ray diffraction analysis, we have studied the structural and morphological evolution of highly Er/Yb co-doped A1203 films in the temperature range from 600℃-900℃. By comparison with TEM observation, the annealing behaviours of photoluminescence （PL） emission and optical loss were found to have relation to the structure and morphology. The increase of PL intensity and optical loss above 800℃ might result from the crystallization of amorphous Al2O3 films. Based on the study on the structure and morphology, a rate equation propagation model of a multilevel system was used to calculate the optical gains of Er-doped Al2O3 planar waveguide amplifiers involving the variation of PL efficiency and optical loss with annealing temperature. It was found that the amplifiers had an optimized optical gain at the temperature corresponding to the minimum of optical loss, rather than at the temperature corresponding to the maximum of PL efficiency, suggesting that the optical loss is a key factor for determining the optical gain of an Er-doped Al2O3 planar waveguide amplifier.展开更多
The purpose of the paper is to develop a mobile Android application--＂Car Log＂ that gives to users the ability to track all the costs for a vehicle and the ability to add fuel cost data by taking a photo of the cash...The purpose of the paper is to develop a mobile Android application--＂Car Log＂ that gives to users the ability to track all the costs for a vehicle and the ability to add fuel cost data by taking a photo of the cash receipt from the respective gas station where the charging was performed. OCR （optical character recognition） is the conversion of images of typed, handwritten or printed text into machine-encoded text. Once we have the text machine-encoded we can further use it in machine processes, like translation, or extracted, meaning text-to-speech transformed, helping people in simple everyday tasks. Users of the application will be able to enter other completely different costs grouped into categories and other charges. Car Log application quickly and easily can visualize, edit and add different costs for a ear. It also supports the ability to add multiple profiles, by entering data for all ears in a single family, for example, or a small business. The test results are positive thus we intend to further develop a cloud ready application.展开更多
This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The go...This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.展开更多
Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.T...Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.展开更多
The development of a knowledge management system for the National Hydro Data Center of Thailand was described in this paper. The system was created after the major flood event in 2011 to improve water resource managem...The development of a knowledge management system for the National Hydro Data Center of Thailand was described in this paper. The system was created after the major flood event in 2011 to improve water resource management. It addresses the need for easy access to water situation reports, which are crucial for informed decision-making on water usage, allocation, and reservoir management. The system utilizes Optical Character Recognition technique to convert scanned water situation reports into searchable text. It applied FastText and ElasticSearch for advanced search functionalities. FastText identified the documents related to the search query, even with typos or misspelled words. ElasticSearch allows for efficient searching of text data based on relevance. The system also integrates Google Search for additional information access. Therefore, this knowledge management system provides an efficient way to access and analyze water situation data in Thailand.展开更多
Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to...Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.展开更多
Traditionally,exam preparation involves manually analyzing past question papers to identify and prioritize key topics.This research proposes a data-driven solution to automate this process using techniques like Docume...Traditionally,exam preparation involves manually analyzing past question papers to identify and prioritize key topics.This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation,Optical Character Recognition(OCR),and Latent Dirichlet Allocation(LDA)for topic modelling.This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers,aiding students in efficient exam preparation.The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify and prioritize key topics.This approach is designed to streamline and optimize exam preparation,making it easier for students to focus on the most relevant topics,thereby using their efforts more effectively.The process involves three stages:(i)Document Layout Segmentation and Data Preparation,using deep learning techniques to separate text from non-textual content in past exam papers,(ii)Text Extraction and Processing using OCR to convert images into machine-readable text,and(iii)Topic Modeling with LDA to identify key topics covered in the exams.The research demonstrates the effectiveness of the proposed method in identifying and prioritizing key topics from exam papers.The LDA model successfully extracts relevant themes,aiding students in focusing their study efforts.The research presents a promising approach for optimizing exam preparation.By leveraging machine learning and topic modelling,the system offers a data-driven and efficient solution for students to prioritize their study efforts.Future work includes expanding the dataset size to further enhance model accuracy.Additionally,integration with educational platforms holds potential for personalized recommendations and adaptive learning experiences.展开更多
In this paper,we have reported the synthesis of FeS2 of higher band gap energy（2.75 eV） by using capping reagent and its successive application in organic-inorganic based hybrid solar cells.Hydrothermal route was ad...In this paper,we have reported the synthesis of FeS2 of higher band gap energy（2.75 eV） by using capping reagent and its successive application in organic-inorganic based hybrid solar cells.Hydrothermal route was adopted for preparing iron pyrite（FeS2） nanoparticles with capping reagent PEG-400.The quality of synthesized FeS2 material was confirmed by X-ray diffraction,field emission scanning electron microscopy,transmission electron microscopy,Fourier transform infrared,thermogravimetric analyzer,and Raman study.The optical band gap energy and electro-chemical band gap energy of the synthesized FeS2 were investigated by UV-vis spectrophotometry and cyclic voltammetry.Finally band gap engineered FeS2 has been successfully used in conjunction with conjugated polymer MEHPPV for harvesting solar energy.The energy conversion efficiency was obtained as 0.064%with a fill-factor of 0.52.展开更多
The silicon carbide(SiC)crystal growth is a multiple-phase aggregation process of Si and C atoms.With the development of the clean energy industry,the 4H-SiC has gained increasing attention as it is an ideal material ...The silicon carbide(SiC)crystal growth is a multiple-phase aggregation process of Si and C atoms.With the development of the clean energy industry,the 4H-SiC has gained increasing attention as it is an ideal material for new energy automobiles and optoelectronic devices.The aggregation process is normally complex and dynamic due to its distinctive formation energy,and it is hard to study and trace back in a non-destructive and comprehensive way.Here,this work developed a non-destructive and deep learning-enhanced characterization method of 4H-SiC material,which was based on micro-CT scanning,the verification of various optical measurements,and the convolutional neural network(ResNet-50 architecture).Harmful defects at the micro-level,polytypes,micropipes,and carbon inclusions could be identified and orientated with more than 96%high performance on both accuracy and precision.The three-dimensional visual reconstruction with quantitative analyses provided a vivid tracing back of the SiC aggregation process.This work demonstrated a use-ful tool to understand and optimize the SiC growth technology and further enhance productivity.展开更多
网络高度发达的信息时代,防止涉密信息被泄露是一件非常重要的任务,尤其是对于政府、军队、公安等重点单位。传统的涉密信息监测系统往往是安装在主机等终端中,无法对于通过手机等智能移动终端偷拍涉密图片或者通过聊天软件上传涉密图...网络高度发达的信息时代,防止涉密信息被泄露是一件非常重要的任务,尤其是对于政府、军队、公安等重点单位。传统的涉密信息监测系统往往是安装在主机等终端中,无法对于通过手机等智能移动终端偷拍涉密图片或者通过聊天软件上传涉密图片的行为无法进行有效的制止。针对这个问题,设计了一种将CTPN文本检测算法、光学字符识别技术(optical character recognition,OCR)与场景识别、图片传输监控相结合的智能移动终端涉密信息监测系统,可广泛应用于Android移动平台中。该系统通过全局扫描,实时相机监察,社交管控三防一体对失泄密行为进行监控监察,有效防止失泄密事故案件的发生。测试结果显示,该系统不仅可以准确识别涉密图片、监测涉密行为并且处理速度快、占用内存空间小,可以满足涉密单位的基本需求。展开更多
Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In ...Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.展开更多
文摘Using a nanoscale silica fiber taper,light can be efficiently coupled into a single ZnO nanowire by means of evanescent coupling. The method is valid for launching light into a single nanowire in the ultraviolet to infrared range with a coupling efficiency of 25%, Low-loss optical guiding of ZnO nanowires is demonstrated, and the photoluminescence of a single ZnO nanowire is also observed. Compared to conventional approaches in which a lensfocused laser beam is used to excite nanowires at specific wavelengths,this evanescent coupling approach has advantages such as high coupling efficiency and broad-band validity, and it is promising for the optical characterization of semiconductor nanowires or nanoribbons.
基金This work was supported by the National Natural Science Foundation of China(No,50376067)the Plan for Science&Technology Development of Guangzhou(2001-Z-117-01).
文摘TiO2 thin film has attracted considerable attention in recent years, due to its different refractive index and transparency with amorphous and different crysta ls in the visible and near-infrared wavelength region, high dielectric constant, wide band gap, high wear resistance and stability, etc, for which make it being used in many fields. This paper aims to investigate the optical characterizatio n of thin film TiO2 on silicon wafer. The TiO2 thin films were prepared by DC re active magnetron sputtering process from Ti target. The reflectivity of the film s was measured by UV-3101PC, and the index of refraction (n) and extinction coef ficient (k) were measured by n & k Analyzer 1200.
基金supported by the National Key Research and Development Program of China(No.2023YFA1608602)the joint Funding from the National Synchrotron Radiation Laboratory(No.KY2090000080)+1 种基金the China Postdoctoral Science Foundation(No.2024M753120)the Fundamental Research Funds for the Central Universities(No.WK2310000126)。
文摘Two-photon polymerization lithography is a technique that provides hundreds of nanometer resolution and full geometric freedom.Several X-ray polymer refractive lenses created by this technique were characterized using differential phase contrast imaging(DPCI)with a microfocus X-ray grating interferometer.The beam deflection angle and wavefront phase shift of the X-ray beam through the lens were obtained.Comparative tests using synchrotron radiation sources showed that the system could measure the surface shape of X-ray refractive lenses with an accuracy of 0.4μm.This study is important for improving the fabrication process and focusing performance of X-ray refractive lenses.
文摘Urdu,a prominent subcontinental language,serves as a versatile means of communication.However,its handwritten expressions present challenges for optical character recognition(OCR).While various OCR techniques have been proposed,most of them focus on recognizing printed Urdu characters and digits.To the best of our knowledge,very little research has focused solely on Urdu pure handwriting recognition,and the results of such proposed methods are often inadequate.In this study,we introduce a novel approach to recognizing Urdu pure handwritten digits and characters using Convolutional Neural Networks(CNN).Our proposed method utilizes convolutional layers to extract important features from input images and classifies them using fully connected layers,enabling efficient and accurate detection of Urdu handwritten digits and characters.We implemented the proposed technique on a large publicly available dataset of Urdu handwritten digits and characters.The findings demonstrate that the CNN model achieves an accuracy of 98.30%and an F1 score of 88.6%,indicating its effectiveness in detecting and classifyingUrdu handwritten digits and characters.These results have far-reaching implications for various applications,including document analysis,text recognition,and language understanding,which have previously been unexplored in the context of Urdu handwriting data.This work lays a solid foundation for future research and development in Urdu language detection and processing,opening up new opportunities for advancement in this field.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korean government(Ministry of Science and ICT)(IITP-2025-RS-2024-00438056).
文摘The increased accessibility of social networking services(SNSs)has facilitated communication and information sharing among users.However,it has also heightened concerns about digital safety,particularly for children and adolescents who are increasingly exposed to online grooming crimes.Early and accurate identification of grooming conversations is crucial in preventing long-term harm to victims.However,research on grooming detection in South Korea remains limited,as existing models trained primarily on English text and fail to reflect the unique linguistic features of SNS conversations,leading to inaccurate classifications.To address these issues,this study proposes a novel framework that integrates optical character recognition(OCR)technology with KcELECTRA,a deep learning-based natural language processing(NLP)model that shows excellent performance in processing the colloquial Korean language.In the proposed framework,the KcELECTRA model is fine-tuned by an extensive dataset,including Korean social media conversations,Korean ethical verification data from AI-Hub,and Korean hate speech data from Hug-gingFace,to enable more accurate classification of text extracted from social media conversation images.Experimental results show that the proposed framework achieves an accuracy of 0.953,outperforming existing transformer-based models.Furthermore,OCR technology shows high accuracy in extracting text from images,demonstrating that the proposed framework is effective for online grooming detection.The proposed framework is expected to contribute to the more accurate detection of grooming text and the prevention of grooming-related crimes.
基金support from the Instituto Nacional de Fotonica-INFoConselho Nacional de Desenvolvimento Científico e Tecnológico-CNPq+2 种基金Coordenacao de Aperfeicoamento de Pessoal de Nível Superior-CAPESFundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco-FACEPEFundacao de Amparo a Pesquisa do Estado de Goiás-FAPEG
文摘The confocal microscopy technique was applied for nonlinear optical characterization of single β-barium-borate（β-BBO） nanocrystals. The experimental setup allows measurements of the laser polarization-selective secondharmonic（SH） generation, and the results can be used to determine the nanocrystals＇ c-axis orientation, as well as to obtain information about their second-order susceptibility χ^（2）. The dependence of the SH signal on the laser polarization allowed the discrimination of individual particles from aggregates. The data were fitted using a model that takes into account the BBO properties and the experimental setup characteristics considering（i） the electrostatic approximation,（ii） the effects of the microscope objective used to focus the light on the sample in an epi-geometry configuration, and（iii） the symmetry of χ^（2） for the β-BBO nanocrystals. A signal at the third-harmonic frequency was also detected, but it was too weak to be studied in detail.
基金Authors are grateful to Concytec-Peru and The World Bank for the financial support of this project under the call“Mejoramiento y Ampliacion de los Servicios del Sistema Nacional de Ciencia Tecnologia e Innovación Tecnologica”8682-PE,through Fondecyt Grant 017-2019 FONDECYT BM INC.INV.
文摘ZnO nanoparticles(ZnO-NP)present innovative optical,electrical,and magnetic properties that depend on spe-cific characteristics,e.g.,size,distribution,and morphology.Thus,these properties are essential to address various applications in areas such as electronics,medicine,energy,and others.In addition,the performance of this ZnO-NP depends of their preparation which can be done by chemical,physical,and biological methods.Meanwhile,nowadays,the main interest in developing ZnO-NP synthesis through biological methods bases on the decrease of use of toxic chemicals or energy applied to the procedures,making the process more cost-effective and environ-mentally friendly.However,the large-scale production of nanoparticles by green synthesis remains a big challenge due to the complexity of the biological extracts used in chemical reactions.That being the case,the preparation of ZnO-NP using Moringa oleifera extract as an alternative biological agent for capping and reduction in synthesis was evaluated in this work.Then,the results based on the analysis of the optical and structural characterization of the ZnO-NP obtained by employing UV-Vis,DLS,zeta potential,XRD,ATR-FTIR,and FE-SEM indicate mostly the presence of spherical nanosized material with a mean hydrodynamic diameter of 47.2 nm measured by DLS and a mean size diameter of 25 nm observed with FE-SEM technique.Furthermore,in FE-SEM images a homo-geneous dispersion and distribution is observed in the absence of agglutination,agglomeration,or generation of significant lumps of the ZnO-NP.The XRD analysis showed that heat annealing induced the crystallite size favor-ing their monocrystallinity.Those obtained data confirm the synthesis of ZnO-NP and the absence of impurities associated with organic compounds in the annealed samples.Finally,those results and low-cost production pre-sent to the synthesized ZnO-NP by this biological method as a useful material in several applications.
文摘This study aims to review the latest contributions in Arabic Optical Character Recognition(OCR)during the last decade,which helps interested researchers know the existing techniques and extend or adapt them accordingly.The study describes the characteristics of the Arabic language,different types of OCR systems,different stages of the Arabic OCR system,the researcher’s contributions in each step,and the evaluationmetrics for OCR.The study reviews the existing datasets for the Arabic OCR and their characteristics.Additionally,this study implemented some preprocessing and segmentation stages of Arabic OCR.The study compares the performance of the existing methods in terms of recognition accuracy.In addition to researchers’OCRmethods,commercial and open-source systems are used in the comparison.The Arabic language is morphologically rich and written cursive with dots and diacritics above and under the characters.Most of the existing approaches in the literature were evaluated on isolated characters or isolated words under a controlled environment,and few approaches were tested on pagelevel scripts.Some comparative studies show that the accuracy of the existing Arabic OCR commercial systems is low,under 75%for printed text,and further improvement is needed.Moreover,most of the current approaches are offline OCR systems,and there is no remarkable contribution to online OCR systems.
基金supported by science and technology projects of Gansu State Grid Corporation of China(52272220002U).
文摘Optical Character Recognition(OCR)refers to a technology that uses image processing technology and character recognition algorithms to identify characters on an image.This paper is a deep study on the recognition effect of OCR based on Artificial Intelligence(AI)algorithms,in which the different AI algorithms for OCR analysis are classified and reviewed.Firstly,the mechanisms and characteristics of artificial neural network-based OCR are summarized.Secondly,this paper explores machine learning-based OCR,and draws the conclusion that the algorithms available for this form of OCR are still in their infancy,with low generalization and fixed recognition errors,albeit with better recognition effect and higher recognition accuracy.Finally,this paper explores several of the latest algorithms such as deep learning and pattern recognition algorithms.This paper concludes that OCR requires algorithms with higher recognition accuracy.
基金Project supported by the National Natural Science Foundation of China （Grant No 50240420656）.
文摘Using transmission electron microscopy （TEM） and x-ray diffraction analysis, we have studied the structural and morphological evolution of highly Er/Yb co-doped A1203 films in the temperature range from 600℃-900℃. By comparison with TEM observation, the annealing behaviours of photoluminescence （PL） emission and optical loss were found to have relation to the structure and morphology. The increase of PL intensity and optical loss above 800℃ might result from the crystallization of amorphous Al2O3 films. Based on the study on the structure and morphology, a rate equation propagation model of a multilevel system was used to calculate the optical gains of Er-doped Al2O3 planar waveguide amplifiers involving the variation of PL efficiency and optical loss with annealing temperature. It was found that the amplifiers had an optimized optical gain at the temperature corresponding to the minimum of optical loss, rather than at the temperature corresponding to the maximum of PL efficiency, suggesting that the optical loss is a key factor for determining the optical gain of an Er-doped Al2O3 planar waveguide amplifier.
文摘The purpose of the paper is to develop a mobile Android application--＂Car Log＂ that gives to users the ability to track all the costs for a vehicle and the ability to add fuel cost data by taking a photo of the cash receipt from the respective gas station where the charging was performed. OCR （optical character recognition） is the conversion of images of typed, handwritten or printed text into machine-encoded text. Once we have the text machine-encoded we can further use it in machine processes, like translation, or extracted, meaning text-to-speech transformed, helping people in simple everyday tasks. Users of the application will be able to enter other completely different costs grouped into categories and other charges. Car Log application quickly and easily can visualize, edit and add different costs for a ear. It also supports the ability to add multiple profiles, by entering data for all ears in a single family, for example, or a small business. The test results are positive thus we intend to further develop a cloud ready application.
基金The results and knowledge included herein have been obtained owing to support from the following institutional grant.Internal grant agency of the Faculty of Economics and Management,Czech University of Life Sciences Prague,Grant No.2023A0004-“Text Segmentation Methods of Historical Alphabets in OCR Development”.https://iga.pef.czu.cz/.Funds were granted to T.Novák,A.Hamplová,O.Svojše,and A.Veselýfrom the author team.
文摘This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions,employing two state-of-the-art deep learning algorithms,namely YOLOv8 and Roboflow 3.0.The goal is to contribute to the preservation and understanding of historical texts,showcasing the potential of modern deep learning methods in archaeological research.Our research culminates in several key findings and scientific contributions.We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context.We also created and annotated an extensive dataset of Palmyrene inscriptions,a crucial resource for further research in the field.The dataset serves for training and evaluating the segmentation models.We employ comparative evaluation metrics to quantitatively assess the segmentation results,ensuring the reliability and reproducibility of our findings and we present custom visualization tools for predicted segmentation masks.Our study advances the state of the art in semi-automatic reading of Palmyrene inscriptions and establishes a benchmark for future research.The availability of the Palmyrene dataset and the insights into algorithm performance contribute to the broader understanding of historical text analysis.
文摘Handwritten character recognition is considered challenging compared with machine-printed characters due to the different human writing styles.Arabic is morphologically rich,and its characters have a high similarity.The Arabic language includes 28 characters.Each character has up to four shapes according to its location in the word(at the beginning,middle,end,and isolated).This paper proposed 12 CNN architectures for recognizing handwritten Arabic characters.The proposed architectures were derived from the popular CNN architectures,such as VGG,ResNet,and Inception,to make them applicable to recognizing character-size images.The experimental results on three well-known datasets showed that the proposed architectures significantly enhanced the recognition rate compared to the baseline models.The experiments showed that data augmentation improved the models’accuracies on all tested datasets.The proposed model outperformed most of the existing approaches.The best achieved results were 93.05%,98.30%,and 96.88%on the HIJJA,AHCD,and AIA9K datasets.
文摘The development of a knowledge management system for the National Hydro Data Center of Thailand was described in this paper. The system was created after the major flood event in 2011 to improve water resource management. It addresses the need for easy access to water situation reports, which are crucial for informed decision-making on water usage, allocation, and reservoir management. The system utilizes Optical Character Recognition technique to convert scanned water situation reports into searchable text. It applied FastText and ElasticSearch for advanced search functionalities. FastText identified the documents related to the search query, even with typos or misspelled words. ElasticSearch allows for efficient searching of text data based on relevance. The system also integrates Google Search for additional information access. Therefore, this knowledge management system provides an efficient way to access and analyze water situation data in Thailand.
文摘Health care is an important part of human life and is a right for everyone. One of the most basic human rights is to receive health care whenever they need it. However, this is simply not an option for everyone due to the social conditions in which some communities live and not everyone has access to it. This paper aims to serve as a reference point and guide for users who are interested in monitoring their health, particularly their blood analysis to be aware of their health condition in an easy way. This study introduces an algorithmic approach for extracting and analyzing Complete Blood Count (CBC) parameters from scanned images. The algorithm employs Optical Character Recognition (OCR) technology to process images containing tabular data, specifically targeting CBC parameter tables. Upon image processing, the algorithm extracts data and identifies CBC parameters and their corresponding values. It evaluates the status (High, Low, or Normal) of each parameter and subsequently presents evaluations, and any potential diagnoses. The primary objective is to automate the extraction and evaluation of CBC parameters, aiding healthcare professionals in swiftly assessing blood analysis results. The algorithmic framework aims to streamline the interpretation of CBC tests, potentially improving efficiency and accuracy in clinical diagnostics.
文摘Traditionally,exam preparation involves manually analyzing past question papers to identify and prioritize key topics.This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation,Optical Character Recognition(OCR),and Latent Dirichlet Allocation(LDA)for topic modelling.This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers,aiding students in efficient exam preparation.The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify and prioritize key topics.This approach is designed to streamline and optimize exam preparation,making it easier for students to focus on the most relevant topics,thereby using their efforts more effectively.The process involves three stages:(i)Document Layout Segmentation and Data Preparation,using deep learning techniques to separate text from non-textual content in past exam papers,(ii)Text Extraction and Processing using OCR to convert images into machine-readable text,and(iii)Topic Modeling with LDA to identify key topics covered in the exams.The research demonstrates the effectiveness of the proposed method in identifying and prioritizing key topics from exam papers.The LDA model successfully extracts relevant themes,aiding students in focusing their study efforts.The research presents a promising approach for optimizing exam preparation.By leveraging machine learning and topic modelling,the system offers a data-driven and efficient solution for students to prioritize their study efforts.Future work includes expanding the dataset size to further enhance model accuracy.Additionally,integration with educational platforms holds potential for personalized recommendations and adaptive learning experiences.
基金supported by University Grants Commission (UGC),Govt.of India under project 39-508/2010(SR)
文摘In this paper,we have reported the synthesis of FeS2 of higher band gap energy（2.75 eV） by using capping reagent and its successive application in organic-inorganic based hybrid solar cells.Hydrothermal route was adopted for preparing iron pyrite（FeS2） nanoparticles with capping reagent PEG-400.The quality of synthesized FeS2 material was confirmed by X-ray diffraction,field emission scanning electron microscopy,transmission electron microscopy,Fourier transform infrared,thermogravimetric analyzer,and Raman study.The optical band gap energy and electro-chemical band gap energy of the synthesized FeS2 were investigated by UV-vis spectrophotometry and cyclic voltammetry.Finally band gap engineered FeS2 has been successfully used in conjunction with conjugated polymer MEHPPV for harvesting solar energy.The energy conversion efficiency was obtained as 0.064%with a fill-factor of 0.52.
基金Fundamental Research Funds for the Central Universities,Grant/Award Number:20720220036National Key Research and Development Program of China,Grant/Award Number:2021YFB3401604+1 种基金Key Scientific and Technological Program of Xiamen,Grant/Award Number:3502Z20231014Innovation Program for Quantum Science and Technology,Grant/Award Number:2021ZD0303400。
文摘The silicon carbide(SiC)crystal growth is a multiple-phase aggregation process of Si and C atoms.With the development of the clean energy industry,the 4H-SiC has gained increasing attention as it is an ideal material for new energy automobiles and optoelectronic devices.The aggregation process is normally complex and dynamic due to its distinctive formation energy,and it is hard to study and trace back in a non-destructive and comprehensive way.Here,this work developed a non-destructive and deep learning-enhanced characterization method of 4H-SiC material,which was based on micro-CT scanning,the verification of various optical measurements,and the convolutional neural network(ResNet-50 architecture).Harmful defects at the micro-level,polytypes,micropipes,and carbon inclusions could be identified and orientated with more than 96%high performance on both accuracy and precision.The three-dimensional visual reconstruction with quantitative analyses provided a vivid tracing back of the SiC aggregation process.This work demonstrated a use-ful tool to understand and optimize the SiC growth technology and further enhance productivity.
文摘网络高度发达的信息时代,防止涉密信息被泄露是一件非常重要的任务,尤其是对于政府、军队、公安等重点单位。传统的涉密信息监测系统往往是安装在主机等终端中,无法对于通过手机等智能移动终端偷拍涉密图片或者通过聊天软件上传涉密图片的行为无法进行有效的制止。针对这个问题,设计了一种将CTPN文本检测算法、光学字符识别技术(optical character recognition,OCR)与场景识别、图片传输监控相结合的智能移动终端涉密信息监测系统,可广泛应用于Android移动平台中。该系统通过全局扫描,实时相机监察,社交管控三防一体对失泄密行为进行监控监察,有效防止失泄密事故案件的发生。测试结果显示,该系统不仅可以准确识别涉密图片、监测涉密行为并且处理速度快、占用内存空间小,可以满足涉密单位的基本需求。
文摘Invoice document digitization is crucial for efficient management in industries.The scanned invoice image is often noisy due to various reasons.This affects the OCR(optical character recognition)detection accuracy.In this paper,letter data obtained from images of invoices are denoised using a modified autoencoder based deep learning method.A stacked denoising autoencoder(SDAE)is implemented with two hidden layers each in encoder network and decoder network.In order to capture the most salient features of training samples,a undercomplete autoencoder is designed with non-linear encoder and decoder function.This autoencoder is regularized for denoising application using a combined loss function which considers both mean square error and binary cross entropy.A dataset consisting of 59,119 letter images,which contains both English alphabets(upper and lower case)and numbers(0 to 9)is prepared from many scanned invoices images and windows true type(.ttf)files,are used for training the neural network.Performance is analyzed in terms of Signal to Noise Ratio(SNR),Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Universal Image Quality Index(UQI)and compared with other filtering techniques like Nonlocal Means filter,Anisotropic diffusion filter,Gaussian filters and Mean filters.Denoising performance of proposed SDAE is compared with existing SDAE with single loss function in terms of SNR and PSNR values.Results show the superior performance of proposed SDAE method.
